Application of 5G-V2X in Traffic Congestion Detection and Mitigation: Field Engineers' Congestion Prediction Based on Data Mining Algorithm

Abstract

The advent of 5G technology has catalyzed transformative applications in various sectors, particularly in traffic management. This abstract delves into the application of 5G-V2X (Vehicle-to-Everything) in traffic congestion detection and mitigation, focusing on field engineers' congestion prediction utilizing data mining algorithms. In congested urban environments, efficient traffic management is paramount for both safety and economic productivity. Leveraging the high-speed and low-latency capabilities of 5G-V2X communication, this study proposes a novel approach to predict and mitigate traffic congestion. Through real-time data collection from vehicles, infrastructure sensors, and traffic management systems, a comprehensive dataset is amassed. Subsequently, advanced data mining algorithms, such as machine learning and deep learning models, are employed to analyze this data and predict congestion patterns with high accuracy. Field engineers equipped with predictive analytics tools can proactively identify congestion hotspots and deploy targeted mitigation strategies, such as adaptive traffic signal control and dynamic route guidance. By integrating 5G-V2X technology with intelligent transportation systems, the proposed framework enhances overall traffic flow efficiency, reduces travel time, and minimizes environmental impact. This abstract highlights the potential of 5G-V2X and data mining algorithms in revolutionizing traffic management, paving the way for smarter and more resilient urban mobility systems.